Generating Natural Posture in an Android
by Mapping Human Posture in Three-Dimensional Position Space
Takashi Minato and Hiroshi Ishiguro
Abstract— In order to develop a robot working in daily
situations, it is necessary to discover the principles relevant toestablishing and maintaining social interaction between humanand robot. One important issue is discovering how to naturally
animate a robot to maintain social interaction. This study
tackles the issue through implementing natural motions in
the android which closely resembles those of human beings.
This paper proposes a method to implement postures that lookhuman by mapping the three-dimensional positions of a humansubject body onto the android.

I. INTRODUCTION
Much effort in recent years has focused on the development of humanoid robots with the aim of communication
with people. However, the design methodology of the robot
has not focused on human-robot communication. Even if
short-term human-robot interaction can be performed by
implementing simple behaviors in a robot, it remains difficult
to realize long-term social interaction. It is therefore necessary to discover the principles relevant to establishing andsupporting social interaction between human and robot. In
other words, a fundamental representation of robot behavior
in daily situations is required.
In order to understand the essence of human-robot communication, it is crucial to investigate the contribution of the
behavior and appearance of humanoid robots. Most researchon interactive robots has focused little on the impact ofappearance. It is not yet clear whether the most comfortableand effective human-robot communication would come froma robot that looks mechanical or human. Some researchers
have evaluated how the behavior of a robot affects humanrobot interaction (e.g., [1]), but in these studies the machinelike appearance of the robot may distort the interpretation ofits behavior. In other words, it may not be straightforward toclarify how the appearance and behavior of the robot affecteach other. This appearance and behavior problem prevents
us from understanding the essence of communication that is
not specific to the robot. In order to tackle this problem, we
adopt an elimination approach, in which we initially build a
robot which has the same motion and appearance as humans
and evaluate the interaction while removing some aspectsof behavior or appearance. In addition, based on the factthat human beings have evolved specialized neural centers
for the detection of bodies and faces [2], [3], [4], [5], we
Takashi Minato is with Asada Project, ERATO, Japan Science andTechnology Agency, 2–1 Yamada-oka, Suita, Osaka 565–0871, Japan(phone:+81-6-6876-8884; fax:+81-6-6876-8994; minato@jeap.org)
Hiroshi Ishiguro is with Department of Adaptive Machine Systems,
Graduate School of Eigineering, Osaka University, 2–1 Yamada-oka, Suita,
Osaka 565–0871, Japan (ishiguro@ams.eng.osaka-u.ac.jp)

can also infer that a humanlike appearance is important. Our
study tackles the appearance and behavior problem throughdevelopment of an android which closely resembles a humanbeing [6].
The human-likeness of the android must be investigated
not only from the standpoint of appearance but also perceptions and motion. The android developed in our study
has a humanlike appearance and a motion mechanism withmany degrees of freedom, which can imitate various humanpostures and gestures. Even a subtle motion such as ashoulder movement caused by breathing can be expressed.
Studies on biological motion have revealed that people candistinguish gender, emotions and differences in exerted effort
from observation of point-light displays of another person’s
motion [7], [8], [9], [10]. This fact suggests that we obtainmuch information from slight differences in people’s postureand motion. The android can also convey such informationin communication with humans by displaying such subtle
motions or slight changes in posture and motion. In addition,
we can examine how these motions influence human-robot
communication using the android.
A straightforward method by which to animate the android
is through implementation of the motion of an actual humansubject, as measured by a motion capture system. Riley etal. [11] and Nakaoka et al. [12] calculated human joint
angles from three-dimensional motion data measured by amotion capture system by solving the inverse kinematics,
and implemented them in the joints of a humanoid robot. In
these studies, the authors assumed the kinematics of the robot
to be similar to that of a human body. However, since theactual kinematics and joint structures are different between
human and robot bodies, calculating the joint angles fromonly the human motion data could in some cases result invisibly different motion. Moreover, evaluating the similarity
between a human motion and the mapped motion of therobot is an important issue. Harada et al. [13] investigated acriterion by which a person evaluates the similarity of a pairof poses or motions of computer graphics human figures.
The authors revealed that a human’s intuitive measure ofsimilarity is enhanced by similarities in the positions ofeach body region rather than the joint angles. Most previous
research transfers the joint angles measured for humans into
the joint angles of the robots to allow for the humanoid
robot to have different forms and sizes. In contrast, since
the shape of an android’s body is similar to that of a person,
the positions of each body region can be compared without
ambiguity. The above-mentioned results suggest that in orderto implement humanlike motion in the android we must

TABLE I
T HE D O F CONFIGURATION OF R EPLIEE Q2.
Degree of freedom
pan×2 + tilt×1
eyebrows×1 + eyelids×1 + cheeks×1
7 (including the upper and lower lips)
3
5×2
2×2
2×2
2×2
4

Eyes
Face
Mouth
Neck
Shoulder
Elbow
Wrist
Fingers
Torso

1

eyes

Fig. 1.

The developed android “Repliee Q2”.

2
parallel link
neck

16

3

15
14

imitate human motion through the similarities of positionsof each body region rather than joint angles.
In order to realize human motion in robots, it is necessary
to generate a sequence of desired postures and to design acontroller to reproduce the sequence, considering the dynamics of the actuators. In this research we divide this problem
into two sub-problems; implementing the human posturesand constructing the controller. This paper proposes a method
to transfer the data gathered on human posture, as measured
by a motion capture system, into a three-dimensional position
space for the android. By defining the posture similarity
between the human and android as the similarity of thesurface shape of the bodies, we can avoid the deviations inposture that occur due to differences in the kinematics. The
method is essential in transferring a person’s posture to an
android which closely resembles a human being.
II. T HE DEVELOPED ANDROID
Fig. 1 shows the developed android called Repliee Q2. The
android is modeled after a Japanese woman. The standingheight is about 160 cm. The skin is composed of a kind ofsilicone that feels like human skin. Forty-two highly sensitive
tactile sensors composed of PVDF film are mounted under
the android’s skin and clothes over the entire body. Since theoutput value of each sensor corresponds to its deforming rate,
the sensors can distinguish different kinds of touch. The soft
skin and tactile sensors give the android a human appearance
and enable natural haptic interaction.
The android is driven by air actuators (air cylinders and air
motors) that give it 42 degrees of freedom (DoFs) from thewaist up. The legs and feet are not powered; it can neither
stand up nor move from a chair. Many air actuators can bemounted in the human-sized body thanks to a high powerto-weight ratio of the actuator.
The configuration of the DoFs is shown in Table I. Fig. 2
shows the kinematic structure of the body, excluding theface and fingers. Some joints are driven by the air motors
and others adopt a slider-crank mechanism. The DoFs of theshoulders enable them to move up and down and backwards
and forwards; this shoulder structure is more complicatedthan that of most existing humanoid robots. Moreover, parallel link mechanisms adopted in some parts complicate the5
6

17
right arm

7

13

4
18
22

8

19
torso 23

20
parallel link

21

left arm

25
parallel link

9
24
10

right hand

12

11
parallel link

left handFig. 2.

Kinematic structure of the android.

kinematics of the android, for example in the waist. The
android can generate a wide range of motions and gestures
as well as various kinds of micro-motions such as theshoulder movements typically caused by human breathing.
Furthermore, the android can make some facial expressions
and mouth shapes. Because the android has servo controllers,
it can be controlled by sending data on the desired joint
angles (cylinder positions and rotor angles) from a hostcomputer. The compliance of the air actuator makes for safer
interaction with movements that are generally smoother thanother systems typically used. Because of the complicateddynamics of the air actuator, executing the trajectory tracking
control is difficult.
III. THE BASIC IDEA
In order for the android to imitate human motion, we
first measure the motion of a human subject using a motion
capture system and obtain a posture sequence by extracting
postures at every keyframe. The android then must reproduce
the given posture sequence. The kinematics and dynamics ofthe developed android are complicated as mentioned above.
Basically, there are two main difficulties:

1) Calculating the android’s joint angles (cylinder positions and rotor angles) to reproduce a target posturefrom the three-dimensional position data by solving
the inverse kinematics of the android in considerationof the difference of kinematics and the deformation of
the silicone skin.
2) Designing a controller to track the desired trajectoriesof the joint angles in consideration of the dynamics ofthe actuators.
We divide the problem of motion mapping from the subject
to the android into these two sub-problems. This paper deals
with the former problem.
In order to transfer the subject’s posture to the android,
it is necessary to calculate suitable joint angles to ensure
the android’s posture resembles that of the subject. Here wemust consider the following aspects:• The appearance of the android closely resembles that ofhumans.
• The joint structure and arrangement of an android are
different from those of a human body. Unlike the jointof an android, the rotation center of the human joint
moves in addition to the rotation.
Since the shape of the android’s body is similar to that ofa person, each body region (e.g. head, elbow, wrist, andshoulder) can be made to correspond without ambiguity.
As a past study has suggested [13], the positions of each
body region should be matched. Most previous research has
assumed the kinematics of a robot to be similar to that ofa human body, and the joint angles of a human have been
implemented as the joint angles of the robot to normalize
their different body shapes. However, using joint angles
calculated only from a human’s posture data could lead to thebody regions being in different positions since the kinematics
of an android actually differs from those of a person. Hence,the positional errors of the body regions should be fed backinto calculation of the joint angles. Our method imitates ahuman posture based on the similarities of positions of each
body region. In other words, it can be said that the method
transfers the person’s surface shape to the android because
the positions of body regions are obtained by measuring
positions of markers attached to the body surface.
In order to calculate the joint angles based on the desired
positions of body regions (three-dimensional position data),
it is necessary to solve the inverse kinematics of the android.
However, this leads to the following difficulties:• The kinematics relating the joint and surface positions
are complicated since the android’s body is covered byan elastic silicone skin and cloth which are not fixed to
the links.
• The inverse kinematics of regions where a parallel linkmechanism is adopted cannot be solved analytically.
We then adopt an optimization method to find the desired
joint angles by minimizing an evaluation function that considers the positional error of all body regions. We canthen avoid dealing with skin deformation in modeling theandroid’s kinematics.

In designing a suitable evaluation function, the following
issues arise:• The range of joint motion of the android is more limited
than that of an average person.
• The search space is large since the android has manydegrees of freedom.
With regard to motion mapping based on joint angles, Pollard
et al. [14] proposed a method to adaptively scale the humanjoint angles to the robot’s range of motion. Their method
considers only differences for individual joints. In contrast,
our method attempts to minimize the positional error without
scaling, because the joint angles of the human subject are notcalculated. Additionally, there is the problem of selecting
which joints are used to compensate the positional error,
owing to limitations in the range of joint motion. Unlike themethod in [14], all joints are used in our method. In respectof the optimization cost, the optimization process needs tobe divided into sub-processes, one for each body part, suchas the head, torso, and left and right arms as shown in [11].
However, it is possible that a difference in hand position
could be compensated by a movement of the waist as wellas the elbow and shoulder. In order to map the posture of
the entire body, it is necessary to compensate for differences
in body shapes by adapting all joint angles. We thus adopt
an evaluation function that considers the positional errors of
all body regions and search the desired joint angles without
splitting the search space.
IV. POSTURE TRANSFER METHODThis section describes the method of transferring theposture of a human subject to the android. The human subject
is assumed to have a similar body shape to the android
and is asked to sit on a chair in the same manner as theandroid is capable of. We use a motion capture system tomeasure the posture of the human subject and the android.
This system can measure the three-dimensional positions ofmarkers attached to the surface of the body in a global
coordinate space. First, some markers are attached to theandroid so that all joint motions can be discriminated. Thenthe same number of markers is attached to corresponding
positions on the subject’s body. Data for both subject andandroid are transformed into a local coordinate system fixedat the center of their bodies. There is no scaling between thetwo coordinate systems.
The marker positions of the subject and android xhi , xai ∈R3 (i = 1, . . . , n) are represented in local coordinates,
where n is the number of markers. Let u ∈ Rm be theandroid’s joint angles, where m is the number of actuatorsthat should be controlled. First, the subject’s posture ismeasured and xh1 , . . . , xhn are obtained. Then, the methodfinds u, which minimizes the following evaluation function
by a hill-climbing search algorithm.
Ep =

n
X

wi δi ,(1)

δi = ||xai − xhi ||,

(2)

i=1

where δi is the distance between corresponding pairs ofmarkers (hereinafter: position error) when both coordinates
are superimposed and w = (w1 , . . . , wn )T is a weight vector
for evaluation of position errors.
Since a hill-climbing search always explores a solution
in the direction in which a given evaluation function is
improved, it is more likely to get stuck in a local optimum (asub-optimal point or plateau that has no superior neighboring
points) than simulated annealing. Regarding this problem, it
is known that multi-objectivization reduces local optima insingle-objective problems [15]. We add another evaluation
function to (1) to reduce the number of local optima in thismethod. In this paper, we use the standard deviation σs determined from the weighted position errors {w1 δ1 , . . . , wn δn },and obtain the following evaluation function.
E = Ep + ασs ,

1(3)
2(4)
5

11
17

6(12)

8

7

18
13

14

Reference Markers

10(16)
9(15)Reference MarkerszxyAndroid Repliee Q2zxyHuman subject

Fig. 3. The marker positions. The number in parenthesis indicates themarker on the opposite side.

(3)

where α is a weight for evaluation of standard deviation. The
search procedure is summarized as follows:
1) Initialize u and calculate E.
2) Generate the next candidate solutions u01 , . . . , u0m as
follows:
u0i = u + gi zei ,(4)where ei is a vector whose i-th element is 1 and whose
other elements are all 0. z is a random variable that
follows a normal distribution N (0, σ). gi is the gain
for the i-th joint’s variation.
3) Evaluate every candidate solution according to (3)and find the candidate u0min which has a minimum
evaluation value Emin .
4) u ← u0min and E ← Emin , if Emin < E.
5) If E does not change for nc steps, the search terminates. Otherwise, go to 2.
V. POSTURE TRANSFER EXPERIMENT
A. Experimental Setup
To verify the proposed method, we conducted an experiment testing how effectively human posture was transferredto the android Repliee Q2. We used only 21 of the android’s
42 DoFs by excluding the 13 DoFs of the face, the 4 of
the wrists, and the 4 of the fingers (m = 21). Eighteen
markers (4 markers for the head, 2 for the chest, 2×2 for theshoulders, 2×2 for the elbows, and 2×2 for the wrists) wereattached to the android as shown in Fig. 3. Another eighteen
markers were attached to corresponding positions on thesubject’s body. The markers attached to the neck and the
belly were not used in this experiment. Because the android’s
waist is fixed, the middle point of the positions of the markers
on the waist set the origin of the android-centered coordinate
system (Fig. 3). The origin of the subject’s coordinate system
was similarly defined. We used a Hawk Digital System1
for motion capture. The system is highly accurate with ameasurement error of less than 1 mm.
1 Motion Analysis Corporation, Santa Rosa, California.

http://www.motionanalysis.com/

Fig. 4. The subject’s postures mapped onto the android (sitting (reference),δe = 28.6mm, σs = 6.5mm).

The weight of the standard deviation α in (3) was set to18.0 so that the positional errors and standard deviation wereof the same order of magnitude. The gains of joint variation
g1 , . . . , gm and σ in (4) were obtained empirically so that themarker movements involved in the joint movements were of
the same order of magnitude. All weights for position error
were set to wi = 1. The parameter defining the convergencecriterion nc was set to 20 steps.
The android closely resembles a human being in appearance to the point that it is possible to convey information
about a situation or emotion by displaying a slight difference

Fig. 5. The subject’s postures mapped onto the android (discouraged,δe = 24.9mm, σs = 6.3mm).

Fig. 6. The subject’s postures mapped onto the android (taking a deep
breath, δe = 21.6mm, σs = 5.5mm).

Fig. 7. The subject’s postures mapped onto the android (avoiding a close
object, δe = 29.7mm, σs = 5.0mm).

in posture. It is meaningful to investigate how a slightdifference in an android posture affects the human-likeness.
We transferred the following five postures to the android(experiment 1).
1) The subject is sitting (reference posture).
2) The subject appears discouraged and has sagging
shoulders.
3) The subject is taking a deep breath.
4) The subject is avoiding an obstacle that appears close
to the face.
5) The subject is holding heavy baggage with one hand.
The four postures (2 to 5) are similar but are slightly different
to the reference posture. We asked the subject to adopt anatural posture while roughly taking into account the limited
range of motion of the android. The initial posture (initial u)
was set by transferring the reference posture using manualcontrol of the android. For the other postures, the mappedreference posture was given as the initial posture.
In addition to the above, toward the goal of motion
mapping, we extracted sequential postures from the subject’s
motion and transferred them to the android (experiment 2).
Fourteen postures were extracted from a guiding behavior
recorded every 166 msec. The postures were transferred
sequentially; the mapped posture was given as the initial u
when transferring the posture of the next time step, with the
exception of the first time step.
B. Experimental Results and Analysis
1) Experiment 1: Figs. 4-8 shows the postures of thesubject and the corresponding postures of the android. In

Fig. 8. The subject’s postures mapped onto the android (holding heavy
baggage, δe = 36.1mm, σs = 12.8mm).

each figure, the right image shows the android. δe and σs are
the average of the position errors and the standard deviation,
respectively. Because the body shapes of the subject andandroid are not identical, the average of the residual position
error is about 30 mm for each marker. In addition, thelimitation of the range of joint motion in the android causes
a residual error. Although α in (3) was determined such that
δe and σs have the same order of magnitude, the position
error was eventually much more weighted.
In Fig. 5, the subject’s shoulders sagged and dropped.
In the mapped posture, the angles of joints 5 and 14(see Fig. 2) had changed so that the android’s shoulders
dropped compared to the reference posture. As a result, aposture indicating discouragement was expressed. However,
the angles of joints 4 and 13 also changed, so that the android
drew back its shoulders even though the subject’s shoulders
were sagged. It seems that this is because the subject bent
the upper body backward.
In Fig. 6, the subject has squared shoulders. In the mapped
posture, the angles of joints 5 and 14 changed so that the
android also had squared shoulders and the angles of joints
4 and 13 changed, drawing the shoulders back. Furthermore,
the angle of joint 22 changed so that the upper body of theandroid was bent backward. As a result, a posture showing a
deep breath was expressed. However, the posture of the head
was not appropriately transferred.
The subject in Fig. 7 was avoiding an obstacle thatappeared close to the face. The right shoulder was drawn
back and the left shoulder dropped. By moving mainly joints
22 and 23 and cylinder 24, the android was able to turn tothe right a little and bend its upper body back and to the left.
As a result, a posture indicating avoidance of a close object
was expressed.
The subject in Fig. 8 had her upper body bent towards
the right-front direction. Although the android does not havean actuator to enable its backbone to bend in the lateral
direction, the android was able to imitate the subject’s upper
body posture by moving mainly joint 22 and cylinder 25.
Since the android cannot straighten its elbow due to thelimited range of joint motion, the right arm pose cannot
be imitated. Therefore, the variance in position error wascomparatively large.
Thus slight differences in posture can be implemented in42

z [mm]

Android

12
3

600
1

6

400
200

Subject

11
13

5
18

8

0

14

16

17

7

15
-200
9
10

-100
0
y [mm]

-400

100
-200
200

0

x [mm]

200

Fig. 9.
The marker trajectories in the subject’s posture sequence andandroid’s posture sequence in the experiment 2. The number indicates themarker ID.

the android by transferring human postures, although there
are positional errors since the body shapes of the subject
and the android are not identical. In the experiments, thehead pose tended not to be appropriately imitated. One of
the causes of this is that the android has no DoF to pushits head forwards. It is necessary to reconsider the marker
places and the evaluation function.
2) Experiment 2: Fig. 10 shows a sequence of postures
where the subject was asked to indicate a direction forthe purpose of guiding someone. The number denotes thesequence. We show the results every two steps due to limited
space. Since the posture of the subject stretching out theleft hand is out of the motion range of the android, theerrors in step 5 to 9 were comparatively large. However, thepostures did have the effect of drawing attention to the left.
Fig.9 shows the marker trajectories in the desired posture
sequence (subject) and mapped posture sequence (android).
It can be seen that there are biases in the positional errors.
The head direction of the android was different from thatof the subject as well as the results in the experiment 1. It
seems that the optimization process got stuck at a suboptimal
solution. The marker places and the evaluation function must
be reconsidered.
In this experiment, we extracted a motion sequence of
postures from the subject and transferred these to the android
toward the goal of motion mapping. From the sequenceof mapped postures, we can obtain the desired trajectories
for the joint angles. However, it is difficult to achieve
accurate trajectory tracking control of the air actuators by
feedback control since the air compressibility acts to increase
the response lag (i.e., dead time is large). Therefore, itis necessary to design a feedforward controller, which isequivalent to the inverse model of the android. Moreover,
we must consider how to define the keyframes from which
the subject’s postures are extracted. Further research on thismatter is required.
C. Comparative Experiments
In order to verify the advantages of the proposed methodof surface shape mapping, we conducted an experiment tocompare it with the existing method proposed by Riley etal. [11]. Their method models a subject’s kinematics using
the same kinematics structure as a target humanoid robot
with the exception of the link size. The joint angles of thesubject are calculated from the three-dimensional positions ofmarkers attached to the surface of the body and are input tothe joints of the robot. Since the physical body size and joint
structure of the subject are different from those of the robot,
the locations of the joint axes in the subject are unknown(the directions of the axes are assumed to be the same as
those of the robot). Then, the locations and angles of thejoints are estimated based on kinematics constraints.
Most previous research, including that of Riley et al. [11],requires that the markers are placed as close to the bone aspossible in order to prevent the markers’ movement differingfrom the bone movement. However, calculating human joint
angles with fixed axis locations could produce a different
pose since the rotation center of the human joint moves with
its rotation. Our method does not have a constraint on themarker places and does not need to consider the moving
rotation center of the joint explicitly. In this experiment, themapping results of our method and the method proposedin [11] were compared using the marker set shown in
Fig. 3. In order to simplify, only the right arm postures were
implemented. The joints of the right arm and waist (joints
13 to 19, and 22) and the markers attached to the right arm(markers 5 to 10, and 17) were used. Eleven postures wereextracted from a handshake motion at every 333 msec. The
method in [11] estimated the subject’s joint locations fromthe eleven postures and calculated the joint angles in eachposture, which were fed to the joints of the android.
The results are shown in Fig. 11. The middle column
shows the captured sequence of the subject’s posture (sixof eleven postures). The left column shows the results of the
proposed method and the right column shows those of themethod in [11]. The subject raised her right hand/wrist above
the elbow level (making an acute angle at the elbow joint)
and offered her hand for a handshake. The subject also raised
her hand above the elbow level when she retracted her hand.
This motion was realized by the android using our method
but not using the method of Riley et al. [11]. It seems that theapproximation of the shoulder joint by a simple joint caused
a displacement of the hand position. The proposed method
is able to avoid this kind of displacement by transferring thesurface shape of the subject.
D. Future Work
In order to realize a humanlike appearance for a human
sized body, the mechanical structure of the android Repliee
Q2 is restricted. From our experiments, we realized that thespine needs to have a DoF to imitate human postures, e.g.,
stooping and extending its head forwards. It is importantto develop a mechanism equivalent to the human spine indevelopment of the android.
Since the body shapes of the subject and Repliee Q2 are
not identical, there must be positional errors. In this experiment, we defined the evaluation function so as to reduce thevariability of the errors: all of the weights w1 , . . . , wn were
set to 1. Harada et al. [13], however, have shown that whencomparing upper body shapes, humans pay more attention
to regions more distant from the body center, such as thehands or head. This result suggests that it is necessary forour method to appropriately define the weights to maximize
a person’s intuitive measure of similarity. Furthermore, thebody regions to be measured should be selected according to
the intuitive measure of similarity even though we attachedthe markers so that all joint motions of the android could
be discriminated. Through experiments that evaluate a person’s intuitive measure of similarity, it will be possible todetermine the appropriate weights and marker places. Thesestudies will bring essential knowledge towards realization ofnatural postures in the android.
VI. SUMMARY
This paper proposed a method for transferring humanposture to the android for the purpose of implementing
natural motion in the android. The method makes use of
the fact that a human’s intuitive measure of similarity is best
achieved by similarities in the positions of each body regionof the android [13]. Most previous methods that assume thekinematics of a robot is similar to that of a person body
produce visibly different postures since the kinematics ofthe robot is actually different. Moreover, the markers need
to be placed as close to the bone as possible to measureaccurate joint angles of a person. The proposed method isable to avoid the displacement caused by different kinematics
structures, the complicated inverse kinematics problem, andthe constraint of marker placement by transferring the surface
shape of a human using a heuristic search method. The
method is also able to inhibit the effect of the accumulated
joint angle error increasing with displacement towards theend of the limbs by assessing the position error of all body
regions. These features are significant for the android, which
has a very humanlike appearance, complicated kinematics,
and soft skin. The experiments showed that slight differencesin posture can be implemented in the android. Furthermore,
it was shown that in some cases the proposed method canreproduce a posture that is more similar to a human’s thanthe existing method.
Although the experiments in this paper did not deal withfacial expression, the proposed method can be applied to thisalso. In particular, if the shape of a flexible body region suchas the lips is controlled by multiple actuators the kinematics
including the skin deformation becomes further complicated.
Our method will also be useful in this case. Whether facial
expressions can be transferred accurately to the android
needs to be clarified. As described above, potential improvements in the search efficiency and evaluation function mustbe further investigated along with more appropriate marker

placement. Furthermore, this method must be integrated witha trajectory-tracking controller for the air actuators to realize
natural motion in the android.
This method is able to not only implement humanlike
posture in the android but serves as a test bed for further
study of human-robot communication. That is to say, themethod enables us to study the essence of human-robot
communication through implementing humanlike posturesand motions in the android.
ACKNOWLEDGEMENT
We have developed the android robot Repliee Q2 incollaboration with Kokoro Company, Ltd.
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1

δe =25.6, σs =3.023δe =26.3, σs =2.653

δe =32.7, σs =7.6

74

δe =30.2, σs =4.998δe =29.5, σs =4.7

119δe =24.9, σs =5.8

1310
δe =26.1, σs =4.8
Fig. 10.

The subject’s postures mapped onto the android (guiding).

Fig. 11. The android postures generated by the proposed method (left) andthe existing method [11] (right).